For describing different degrees of reproducibility, the participants of Dagstuhl Seminar 16041 started from a model referring to the components of an experiment: R: the research goal, M: the method proposed for achieving this goal, I: the implementation of this method, P: the platform on which the implementation is run, D: the data (input + parameters) used in the experiment, and finally A: the actor performing the experiment. When a researcher tries to reproduce an experiment, he should specify which components are changed, i.e. 'primed': R -> R' repurpose for a new research goal, M -> M': a new method, I -> I': alternative implementation, P -> P': different platform, D -> D': other input/parameters. Finally, other important aspects of reproducibility are consistency of experimental results, and transparency, i.e. the ability to look into all necessary components to verify that the experiment does what it claims.